{"id":"W2026750350","doi":"10.1016/j.aap.2007.03.008","title":"Bayesian multiple testing procedures for hotspot identification","year":2007,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Université Laval","funders":"","keywords":"Bayesian probability; Poisson distribution; Hotspot (geology); Posterior probability; Computer science; Data mining; Bayesian hierarchical modeling; Negative binomial distribution; Statistical hypothesis testing; Bayesian inference; Machine learning; Statistics; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141463,0.0001366326,0.0002213236,0.0002889338,0.0002867945,0.00009702628,0.0001635382,0.00007503366,0.000189685],"category_scores_gemma":[0.00498747,0.0001410207,0.00025348,0.001342515,0.00002840281,0.0001930076,0.00002322386,0.00006500529,0.00003265504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009217743,"about_ca_system_score_gemma":0.00003465559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001884431,"about_ca_topic_score_gemma":0.00146112,"domain_scores_codex":[0.9983514,0.00003879068,0.0007558571,0.0003311232,0.0002805727,0.0002422748],"domain_scores_gemma":[0.9975993,0.001135743,0.0004414257,0.0003039032,0.0004250172,0.0000945945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001034249,0.001267274,0.3366107,0.00009210011,0.001450429,0.000001865251,0.0003466348,0.0006114233,0.01216842,0.5747311,0.005667354,0.0669493],"study_design_scores_gemma":[0.0005677232,0.00003032198,0.6253668,0.00003520169,0.002219706,0.000002186252,0.0002279394,0.07793584,0.004478097,0.2886845,0.0001677149,0.0002839087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06444082,0.00001494265,0.9342381,0.0001451579,0.00003493802,0.0006693925,0.000005993658,0.0001509556,0.00029971],"genre_scores_gemma":[0.9264917,0.000001880374,0.07189284,0.00002790857,0.0000665457,0.0002121613,0.0005607496,0.00001475504,0.0007315122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8623452,"threshold_uncertainty_score":0.5970828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09166654922099575,"score_gpt":0.4148263804008313,"score_spread":0.3231598311798355,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}